Airborne flux measurements of trace species in an Arctic boundary layer

1992 ◽  
Vol 97 (D15) ◽  
pp. 16601 ◽  
Author(s):  
John A. Ritter ◽  
John D. W. Barrick ◽  
Glen W. Sachse ◽  
Gerald L. Gregory ◽  
Mary A. Woerner ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Arctic Boundary Layer ◽  
Trace Species ◽  
Flux Measurements ◽  
Airborne Flux Measurements

Airborne boundary layer flux measurements of trace species over Canadian boreal forest and northern wetland regions

1994 ◽  
Vol 99 (D1) ◽  
pp. 1671 ◽  
Author(s):  
John A. Ritter ◽  
John D. W. Barrick ◽  
Catherine E. Watson ◽  
Glen W. Sachse ◽  
Gerald L. Gregory ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Boreal Forest ◽  
Trace Species ◽  
Flux Measurements ◽  
Canadian Boreal Forest

Airborne flux measurements and budget estimates of trace species over the Amazon Basin during the GTE/ABLE 2B expedition

1990 ◽  
Vol 95 (D10) ◽  
pp. 16875 ◽  
Author(s):  
John A. Ritter ◽  
Donald H. Lenschow ◽  
John D. W. Barrick ◽  
Gerald L. Gregory ◽  
Glen W. Sachse ◽  
...  
Keyword(s):  
Amazon Basin ◽  
Trace Species ◽  
Flux Measurements ◽  
Airborne Flux Measurements

Mesoscale Variability in the Summer Arctic Boundary Layer

2009 ◽  
Vol 130 (3) ◽  
pp. 383-406 ◽  
Author(s):  
Michael Tjernström ◽  
Thorsten Mauritsen
Keyword(s):  
Boundary Layer ◽  
Mesoscale Variability ◽  
Arctic Boundary Layer

Observation of coinciding arctic boundary layer ozone depletion and snow surface emissions of nitrous acid

2006 ◽  
Vol 40 (11) ◽  
pp. 1949-1956 ◽  
Author(s):  
Antonio Amoroso ◽  
Harry J. Beine ◽  
Roberto Sparapani ◽  
Marianna Nardino ◽  
Ivo Allegrini
Keyword(s):  
Boundary Layer ◽  
Ozone Depletion ◽  
Nitrous Acid ◽  
Snow Surface ◽  
Arctic Boundary Layer ◽  
Boundary Layer Ozone

scholarly journals Airborne observations and simulations of three-dimensional radiative interactions between Arctic boundary layer clouds and ice floes

2015 ◽  
Vol 15 (14) ◽  
pp. 8147-8163 ◽  
Author(s):  
M. Schäfer ◽  
E. Bierwirth ◽  
A. Ehrlich ◽  
E. Jäkel ◽  
M. Wendisch
Keyword(s):  
Boundary Layer ◽  
Radiative Transfer ◽  
Sea Ice ◽  
Three Dimensional ◽  
Open Water ◽  
Radiative Effects ◽  
Arctic Boundary Layer ◽  
Boundary Layer Clouds ◽  
Ice Edge ◽  
Ice Floes

Abstract. Based on airborne spectral imaging observations, three-dimensional (3-D) radiative effects between Arctic boundary layer clouds and highly variable Arctic surfaces were identified and quantified. A method is presented to discriminate between sea ice and open water under cloudy conditions based on airborne nadir reflectivity γλ measurements in the visible spectral range. In cloudy cases the transition of γλ from open water to sea ice is not instantaneous but horizontally smoothed. In general, clouds reduce γλ above bright surfaces in the vicinity of open water, while γλ above open sea is enhanced. With the help of observations and 3-D radiative transfer simulations, this effect was quantified to range between 0 and 2200 m distance to the sea ice edge (for a dark-ocean albedo of αwater = 0.042 and a sea-ice albedo of αice = 0.91 at 645 nm wavelength). The affected distance Δ L was found to depend on both cloud and sea ice properties. For a low-level cloud at 0–200 m altitude, as observed during the Arctic field campaign VERtical Distribution of Ice in Arctic clouds (VERDI) in 2012, an increase in the cloud optical thickness τ from 1 to 10 leads to a decrease in Δ L from 600 to 250 m. An increase in the cloud base altitude or cloud geometrical thickness results in an increase in Δ L; for τ = 1/10 Δ L = 2200 m/1250 m in case of a cloud at 500–1000 m altitude. To quantify the effect for different shapes and sizes of ice floes, radiative transfer simulations were performed with various albedo fields (infinitely long straight ice edge, circular ice floes, squares, realistic ice floe field). The simulations show that Δ L increases with increasing radius of the ice floe and reaches maximum values for ice floes with radii larger than 6 km (500–1000 m cloud altitude), which matches the results found for an infinitely long, straight ice edge. Furthermore, the influence of these 3-D radiative effects on the retrieved cloud optical properties was investigated. The enhanced brightness of a dark pixel next to an ice edge results in uncertainties of up to 90 and 30 % in retrievals of τ and effective radius reff, respectively. With the help of Δ L, an estimate of the distance to the ice edge is given, where the retrieval uncertainties due to 3-D radiative effects are negligible.

scholarly journals An aircraft-borne chemical ionization – ion trap mass spectrometer (CI-ITMS) for fast PAN and PPN measurements

2010 ◽  
Vol 3 (5) ◽  
pp. 4313-4354
Author(s):  
A. Roiger ◽  
H. Aufmhoff ◽  
P. Stock ◽  
F. Arnold ◽  
H. Schlager
Keyword(s):  
Boundary Layer ◽  
Mass Spectrometer ◽  
Chemical Ionization ◽  
Ion Trap ◽  
The Arctic ◽  
Arctic Boundary Layer ◽  
Online Calibration ◽  
Mixing Ratios ◽  
Ion Trap Mass Spectrometer ◽  
Research Aircraft

Abstract. An airborne chemical ionization ion trap mass spectrometer instrument (CI-ITMS) has been developed for tropospheric and stratospheric fast in-situ measurements of PAN (peroxyacetyl nitrate) and PPN (peroxypropionyl nitrate). The first scientific deployment of the FASTPEX instrument (FASTPEX = Fast Measurement of Peroxyacyl nitrates) took place in the Arctic during 18 missions aboard the DLR research aircraft Falcon, within the framework of the POLARCAT-GRACE campaign in the summer of 2008. The FASTPEX instrument is described and characteristic properties of the employed ion trap mass spectrometer are discussed. Atmospheric data obtained at altitudes of up to ~12 km are presented, from the boundary layer to the lowermost stratosphere. Data were sampled with a time resolution of 2 s and a 2σ detection limit of 25 pmol mol−1. An isotopically labelled standard was used for a permanent online calibration. For this reason the accuracy of the PAN measurements is better than ±10% for mixing ratios greater than 200 pmol mol−1. PAN mixing ratios in the summer Arctic troposphere were in the order of a few hundred pmol mol−1 and generally correlated well with CO. In the Arctic boundary layer and lowermost stratosphere smaller PAN mixing ratios were observed due to a combination of missing local sources of PAN precursor gases and efficient removal processes (thermolysis/photolysis). PPN, the second most abundant PAN homologue, was measured simultanously. Observed PPN/PAN ratios range between ~0.03 and 0.3.

scholarly journals Spatial resolution and regionalization of airborne flux measurements using environmental response functions

2012 ◽  
Vol 9 (11) ◽  
pp. 15937-16003 ◽  
Author(s):  
S. Metzger ◽  
W. Junkermann ◽  
M. Mauder ◽  
K. Butterbach-Bahl ◽  
B. Trancón y Widemann ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Boosted Regression Trees ◽  
Environmental Response ◽  
Catchment Scale ◽  
River Catchment ◽  
Flux Measurements ◽  
Xilin River Catchment ◽  
Airborne Flux Measurements

Abstract. The goal of this study is to characterize the sensible (H) and latent (LE) heat exchange for different land covers in the heterogeneous steppe landscape of the Xilin River Catchment, Inner Mongolia, China. Eddy-covariance flux measurements at 50–100 m above ground were conducted in July 2009 using a weight-shift microlight aircraft. Wavelet decomposition of the turbulence data enables a spatial discretization of 90 m of the flux measurements. For a total of 8446 flux observations during 12 flights, MODIS land surface temperature (LST) and enhanced vegetation index (EVI) in each flux footprint are determined. Boosted regression trees are then used to infer an environmental response function (ERF) between all flux observations (H, LE) and biophysical- (LST, EVI) and meteorological drivers. Numerical tests show that ERF predictions covering the entire Xilin River Catchment (&amp;approx; 3670 km2) are accurate to ≤ 18%. The predictions are then summarized for each land cover type, providing individual estimates of source strength (36 W m−2 < H < 364 W m−2, 46 W m−2 < LE < 425 W m−2) and spatial variability (11 W m−2 < σH < 169 W m−2, 14 W m−2 < σLE < 152 W m−2) to a precision of ≤ 5%. Lastly, ERF predictions of land cover specific Bowen ratios are compared between subsequent flights at different locations in the Xilin River Catchment. Agreement of the land cover specific Bowen ratios to within 12 ± 9% emphasizes the robustness of the presented approach. This study indicates the potential of ERFs for (i) extending airborne flux measurements to the catchment scale, (ii) assessing the spatial representativeness of long-term tower flux measurements, and (iii) designing, constraining and evaluating flux algorithms for remote sensing and numerical modelling applications.

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